Depth-from-stereo bending correction using visual inertial odometry features

ABSTRACT

A method for correcting a bending of a flexible device is described. In one aspect, the method includes accessing feature data of a first stereo frame that is generated by stereo optical sensors of the flexible device, the feature data generated based on a visual-inertial odometry (VIO) system of the flexible device, accessing depth map data of the first stereo frame, the depth map data generated based on a depth map system of the flexible device, estimating a pitch-roll bias and a yaw bias based on the features data and the depth map data of the first stereo frame, and generating a second stereo frame after the first stereo frame, the second stereo frame based on the pitch-roll bias and the yaw bias of the first stereo frame.

CLAIM OF PRIORITY

The present application claims priority to U.S. Provisional PatentApplication Ser. No. 63/188,815, filed May 14, 2021, which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to a visualtracking system. Specifically, the present disclosure addresses systemsand methods for mitigating bending effects in visual-inertial trackingsystems.

BACKGROUND

An augmented reality (AR) device enables a user to observe a scene whilesimultaneously seeing relevant virtual content that may be aligned toitems, images, objects, or environments in the field of view of thedevice. A virtual reality (VR) device provides a more immersiveexperience than an AR device. The VR device blocks out the field of viewof the user with virtual content that is displayed based on a positionand orientation of the VR device.

Both AR and VR devices rely on motion tracking systems that track a pose(e.g., orientation, position, location) of the device. The motiontracking system is typically factory calibrated (based on predefinedrelative positions between the cameras and other sensors) to accuratelydisplay the virtual content at a desired location relative to itsenvironment. However, factory calibration parameters can drift over timeas the user wears the AR/VR device due to mechanical stress andtemperature changes in the AR/VR device.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced.

FIG. 1 is a block diagram illustrating an environment for operating anAR/VR display device in accordance with one example embodiment.

FIG. 2 is a block diagram illustrating an AR/VR display device inaccordance with one example embodiment.

FIG. 3 is a block diagram illustrating a visual tracking system inaccordance with one example embodiment.

FIG. 4 is a block diagram illustrating a bending correction module inaccordance with one example embodiment.

FIG. 5 is a block diagram illustrating a pitch-roll bending module inaccordance with one example embodiment.

FIG. 6 is a block diagram illustrating a yaw bending module inaccordance with one example embodiment.

FIG. 7 is a block diagram illustrating a corrected depth frame inaccordance with one embodiment.

FIG. 8 is a flow diagram illustrating a method for adjusting acoordinate system in accordance with one example embodiment.

FIG. 9 is a flow diagram illustrating a method for adjusting a yaw biasin accordance with one example embodiment.

FIG. 10 is a flow diagram illustrating a method for updating the totalyaw bending estimation in accordance with one example embodiment.

FIG. 11 illustrates misalignment errors resulting from bending of aflexible device in accordance with one embodiment.

FIG. 12 illustrates a pitch-roll misalignment in accordance with oneembodiment.

FIG. 13 illustrates depth misalignment on a projected two-dimensionalmap in accordance with one embodiment.

FIG. 14 illustrates a network environment in which a head-wearabledevice can be implemented according to one example embodiment.

FIG. 15 is block diagram showing a software architecture within whichthe present disclosure may be implemented, according to an exampleembodiment.

FIG. 16 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, according to one example embodiment.

DETAILED DESCRIPTION

The description that follows describes systems, methods, techniques,instruction sequences, and computing machine program products thatillustrate example embodiments of the present subject matter. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth in order to provide an understanding of variousembodiments of the present subject matter. It will be evident, however,to those skilled in the art, that embodiments of the present subjectmatter may be practiced without some or other of these specific details.Examples merely typify possible variations. Unless explicitly statedotherwise, structures (e.g., structural components, such as modules) areoptional and may be combined or subdivided, and operations (e.g., in aprocedure, algorithm, or other function) may vary in sequence or becombined or subdivided.

The term “augmented reality” (AR) is used herein to refer to aninteractive experience of a real-world environment where physicalobjects that reside in the real-world are “augmented” or enhanced bycomputer-generated digital content (also referred to as virtual contentor synthetic content). AR can also refer to a system that enables acombination of real and virtual worlds, real-time interaction, and 3Dregistration of virtual and real objects. A user of an AR systemperceives virtual content that appears to be attached or interact with areal-world physical object.

The term “virtual reality” (VR) is used herein to refer to a simulationexperience of a virtual world environment that is completely distinctfrom the real-world environment. Computer-generated digital content isdisplayed in the virtual world environment. VR also refers to a systemthat enables a user of a VR system to be completely immersed in thevirtual world environment and to interact with virtual objects presentedin the virtual world environment.

The term “AR application” is used herein to refer to a computer-operatedapplication that enables an AR experience. The term “VR application” isused herein to refer to a computer-operated application that enables aVR experience. The term “AR/VR application” refers to acomputer-operated application that enables a combination of an ARexperience or a VR experience.

The term “visual tracking system” is used herein to refer to acomputer-operated application or system that enables a system to trackvisual features identified in images captured by one or more cameras ofthe visual tracking system. The visual tracking system builds a model ofa real-world environment based on the tracked visual features.Non-limiting examples of the visual tracking system include: a visualSimultaneous Localization and Mapping system (VSLAM), and VisualInertial Odometry (VIO) system. VSLAM can be used to build a target froman environment, or a scene based on one or more cameras of the visualtracking system. VIO (also referred to as a visual-inertial trackingsystem) determines a latest pose (e.g., position and orientation) of adevice based on data acquired from multiple sensors (e.g., opticalsensors, inertial sensors) of the device.

The term “Inertial Measurement Unit” (IMU) is used herein to refer to adevice that can report on the inertial status of a moving body includingthe acceleration, velocity, orientation, and position of the movingbody. An IMU enables tracking of movement of a body by integrating theacceleration and the angular velocity measured by the IMU. IMU can alsorefer to a combination of accelerometers and gyroscopes that candetermine and quantify linear acceleration and angular velocity,respectively. The values obtained from the IMUs gyroscopes can beprocessed to obtain the pitch, roll, and heading of the IMU and,therefore, of the body with which the IMU is associated. Signals fromthe IMU's accelerometers also can be processed to obtain velocity anddisplacement of the IMU.

The term “flexible device” is used herein to refer to a device that iscapable of bending without breaking. Non-limiting examples of flexibledevices include: head-worn devices such as glasses, flexible displaydevices such as AR/VR glasses, or any other wearable devices that arecapable of bending without breaking to fit a body part of the user.

Both AR and VR applications allow a user to access information, such asin the form of virtual content rendered in a display of an AR/VR displaydevice (also referred to as a display device, flexible device, flexibledisplay device). The rendering of the virtual content may be based on aposition of the display device relative to a physical object or relativeto a frame of reference (external to the display device) so that thevirtual content correctly appears in the display. For AR, the virtualcontent appears aligned with a physical object as perceived by the userand a camera of the AR display device. The virtual content appears to beattached to the physical world (e.g., a physical object of interest). Todo this, the AR display device detects the physical object and tracks apose of the AR display device relative to the position of the physicalobject. A pose identifies a position and orientation of the displaydevice relative to a frame of reference or relative to another object.For VR, the virtual object appears at a location based on the pose ofthe VR display device. The virtual content is therefore refreshed basedon the latest pose of the device. A visual tracking system at thedisplay device determines the pose of the display device.

Flexible devices that include a visual tracking system can operate onstereo vision using two cameras that are mounted on the flexible device.For example, one camera is mounted to a left temple of a frame of theflexible device, and another camera is mounted to the right temple ofthe frame of the flexible device. However, the flexible device can bendto accommodate different user head sizes. As such, the bending canresult in undesirable shift (away from factory calibrated configuration)in the stereo images that can lead to errors in depth sensing using theshifted stereo images.

One method to compensate for the bending is to match features per frameon a left image and a right image and optimizing a symmetrical model(where cameras are rotated in a symmetrical manner so that only threeangles are left to be solved). However, such per-frame optimization canresult in additional computation time. Furthermore, the per-frameoptimization is more suitable for outdoor environments where the distantfeatures can be used for anchoring. In contrast, for indoor settings,objects are typically closer to the flexible device and thus the yawestimation may be unstable.

The present application describes a method for estimating andcompensating for the bending of flexible stereo-to-depth devices byusing VIO stereo matches to validate rectification. In one example, themethod includes validating the rectification by “projecting” stereo VIOfeatures from the “native” camera to a rectified coordinate system, andtriggering a bending compensation process when the VIO matches do notlie on a same raster line.

In one example embodiment, a method for correcting a bending of aflexible device is described. In one aspect, the method includesaccessing feature data of a first stereo frame that is generated bystereo optical sensors of the flexible device, the feature datagenerated based on a VIO system of the flexible device, accessing depthmap data of the first stereo frame, the depth map data generated basedon a depth map system of the flexible device, estimating a pitch-rollbias and a yaw bias based on the features data and the depth map data ofthe first stereo frame, and generating a second stereo frame after thefirst stereo frame, the second stereo frame based on the pitch-roll biasand the yaw bias of the first stereo frame.

As a result, one or more of the methodologies described hereinfacilitate solving the technical problem of inaccurate depth sensingfrom stereo extraction of a flexible device. In other words, the bendingof the flexible device causes errors in the depth sensing. The presentlydescribed method provides an improvement to an operation of thefunctioning of a computing device by rectifying the depth map from aflexible stereo-to-depth device that is bent. As such, one or more ofthe methodologies described herein may obviate a need for certainefforts or computing resources. Examples of such computing resourcesinclude processor cycles, network traffic, memory usage, data storagecapacity, power consumption, network bandwidth, and cooling capacity.

FIG. 1 is a network diagram illustrating an environment 100 suitable foroperating an AR/VR display device 106, according to some exampleembodiments. The environment 100 includes a user 102, an AR/VR displaydevice 106, and a physical object 104. A user 102 operates the AR/VRdisplay device 106. The user 102 may be a human user (e.g., a humanbeing), a machine user (e.g., a computer configured by a softwareprogram to interact with the AR/VR display device 106), or any suitablecombination thereof (e.g., a human assisted by a machine or a machinesupervised by a human). The user 102 is associated with the AR/VRdisplay device 106.

The AR/VR display device 106 includes a flexible device. In one example,the flexible device includes a computing device with a display such as asmartphone, a tablet computer, or a wearable computing device (e.g.,watch or glasses). The computing device may be hand-held or may beremovable mounted to a head of the user 102. In one example, the displayincludes a screen that displays images captured with a camera of theAR/VR display device 106. In another example, the display of the devicemay be transparent such as in lenses of wearable computing glasses. Inother examples, the display may be non-transparent, partiallytransparent, partially opaque. In yet other examples, the display may bewearable by the user 102 to cover the field of vision of the user 102.

The AR/VR display device 106 includes an AR application that generatesvirtual content based on images detected with the camera of the AR/VRdisplay device 106. For example, the user 102 may point a camera of theAR/VR display device 106 to capture an image of the physical object 104.The AR application generates virtual content corresponding to anidentified object (e.g., physical object 104) in the image and presentsthe virtual content in a display of the AR/VR display device 106.

The AR/VR display device 106 includes a visual tracking system 108. Thevisual tracking system 108 tracks the pose (e.g., position andorientation) of the AR/VR display device 106 relative to the real worldenvironment 110 using, for example, optical sensors (e.g., depth-enabled3D camera, image camera), inertia sensors (e.g., gyroscope,accelerometer), wireless sensors (Bluetooth, Wi-Fi), GPS sensor, andaudio sensor. The visual tracking system 108 can include a VIO system.In one example, the AR/VR display device 106 displays virtual contentbased on the pose of the AR/VR display device 106 relative to the realworld environment 110 and/or the physical object 104.

Any of the machines, databases, or devices shown in FIG. 1 may beimplemented in a general-purpose computer modified (e.g., configured orprogrammed) by software to be a special-purpose computer to perform oneor more of the functions described herein for that machine, database, ordevice. For example, a computer system able to implement any one or moreof the methodologies described herein is discussed below with respect toFIG. 8 to FIG. 10. As used herein, a “database” is a data storageresource and may store data structured as a text file, a table, aspreadsheet, a relational database (e.g., an object-relationaldatabase), a triple store, a hierarchical data store, or any suitablecombination thereof. Moreover, any two or more of the machines,databases, or devices illustrated in FIG. 1 may be combined into asingle machine, and the functions described herein for any singlemachine, database, or device may be subdivided among multiple machines,databases, or devices.

The AR/VR display device 106 may operate over a computer network. Thecomputer network may be any network that enables communication betweenor among machines, databases, and devices. Accordingly, the computernetwork may be a wired network, a wireless network (e.g., a mobile orcellular network), or any suitable combination thereof. The computernetwork may include one or more portions that constitute a privatenetwork, a public network (e.g., the Internet), or any suitablecombination thereof.

FIG. 2 is a block diagram illustrating modules (e.g., components) of theAR/VR display device 106, according to some example embodiments. TheAR/VR display device 106 includes sensors 202, a display 204, aprocessor 208, and a storage device 206. Examples of AR/VR displaydevice 106 include a wearable computing device, a desktop computer, avehicle computer, a tablet computer, a navigational device, a portablemedia device, or a smart phone.

The sensors 202 include, for example, an optical sensors 212 (e.g.,camera such as a color camera, a thermal camera, a depth sensor and oneor multiple grayscale, global shutter tracking cameras) and an inertialsensors 214 (e.g., gyroscope, accelerometer). Other examples of sensors202 include a proximity or location sensor (e.g., near fieldcommunication, GPS, Bluetooth, Wifi), an audio sensor (e.g., amicrophone), or any suitable combination thereof. It is noted that thesensors 202 described herein are for illustration purposes and thesensors 202 are thus not limited to the ones described above.

The display 204 includes a screen or monitor configured to displayimages generated by the processor 208. In one example embodiment, thedisplay 204 may be transparent or semi-opaque so that the user 102 cansee through the display 204 (in AR use case). In another exampleembodiment, the display 204 covers the eyes of the user 102 and blocksout the entire field of view of the user 102 (in VR use case). Inanother example, the display 204 includes a touchscreen displayconfigured to receive a user input via a contact on the touchscreendisplay.

The processor 208 includes an AR/VR application 210, a visual trackingsystem 108, and a bending correction module 216. The AR/VR application210 detects and identifies a physical environment or the physical object104 using computer vision. The AR/VR application 210 retrieves a virtualobject (e.g., 3D object model) based on the identified physical object104 or physical environment. The AR/VR application 210 renders thevirtual object in the display 204. For an AR application, the AR/VRapplication 210 includes a local rendering engine that generates avisualization of a virtual object overlaid (e.g., superimposed upon, orotherwise displayed in tandem with) on an image of the physical object104 captured by the optical sensors 212. A visualization of the virtualobject may be manipulated by adjusting a position of the physical object104 (e.g., its physical location, orientation, or both) relative to theoptical sensors 212. Similarly, the visualization of the virtual objectmay be manipulated by adjusting a pose of the AR/VR display device 106relative to the physical object 104. For a VR application, the AR/VRapplication 210 displays the virtual object in the display 204 at alocation (in the display 204) determined based on a pose of the AR/VRdisplay device 106.

The visual tracking system 108 estimates a pose of the AR/VR displaydevice 106. For example, the visual tracking system 108 uses image dataand corresponding inertial data from the optical sensors 212 and theinertial sensors 214 to track a location and pose of the AR/VR displaydevice 106 relative to a frame of reference (e.g., real worldenvironment 110). In one example, the visual tracking system 108includes a VIO system as previously described above.

The bending correction module 216 accesses VIO data from the VIO of thevisual tracking system 108 to estimate a pitch-roll bias and yaw bendingbias. The bending correction module 216 rectifies the biases to mitigateany depth-sensing errors from the bending. In one example embodiment,the bending correction module 216 estimates and compensates the bendingof flexible stereo-to-depth devices by using VIO stereo matches tovalidate rectification. The bending correction module 216 validates therectification by “projecting” stereo VIO features from the “native”camera to a rectified coordinate system. The bending correction module216 triggers a bending compensation process when the VIO matches do notlie on the same raster line. Example components of the bendingcorrection module 216 are described in more detail below with respect toFIG. 4.

The storage device 206 stores virtual object content 218 and bias data220. The bias data 220 include values of the estimated pitch-roll biasand yaw bending bias of the AR/VR display device 106. The virtual objectcontent 218 includes, for example, a database of visual references(e.g., images) and corresponding experiences (e.g., three-dimensionalvirtual objects, interactive features of the three-dimensional virtualobjects).

Any one or more of the modules described herein may be implemented usinghardware (e.g., a processor of a machine) or a combination of hardwareand software. For example, any module described herein may configure aprocessor to perform the operations described herein for that module.Moreover, any two or more of these modules may be combined into a singlemodule, and the functions described herein for a single module may besubdivided among multiple modules. Furthermore, according to variousexample embodiments, modules described herein as being implementedwithin a single machine, database, or device may be distributed acrossmultiple machines, databases, or devices.

FIG. 3 illustrates the visual tracking system 108 in accordance with oneexample embodiment. The visual tracking system 108 includes, forexample, a VIO system 302 and a depth map system 304. The VIO system 302accesses inertial sensor data from the inertial sensors 214 and imagesfrom the optical sensors 212.

The VIO system 302 determines a pose (e.g., location, position,orientation) of the AR/VR display device 106 relative to a frame ofreference (e.g., real world environment 110). In one example embodiment,the VIO system 302 estimates the pose of the AR/VR display device 106based on 3D maps of feature points from images captured with the opticalsensors 212 and the inertial sensor data captured with the inertialsensors 214.

The depth map system 304 accesses image data from the optical sensors212 and generates a depth map based on the VIO data (e.g., featurepoints depth) from the VIO system 302. For example, the depth map system304 generates a depth map based on the depth of matched features betweena left image (generated by a left side camera) and a right image(generated by a right side camera). In another example, the depth mapsystem 304 is based on triangulation of element disparities in thestereo images.

FIG. 4 is a block diagram illustrating a bending correction module 216in accordance with one example embodiment. The bending correction module216 includes a pitch-roll bending module 402, a yaw bending module 404,and a mitigation module 406.

The pitch-roll bending module 402 determines whether the mitigationmodule 406 is to rectify a bending that results in a pitch or rolldeviation of the flexible device. In one example, the pitch-roll bendingmodule 402 projects stereo VIO features from the optical sensors 212 toa rectified coordinate system. The pitch-roll bending module 402triggers the mitigation module 406 when VIO features matches do not lieon the same raster line.

The yaw bending module 404 determines whether the mitigation module 406is to rectify a bending that results in a yaw deviation of the flexibledevice. In one example, the yaw bending module 404 estimates the yawbias by accessing 3D landmarks determined by the VIO to obtain a widebaseline with temporal consistency.

The mitigation module 406 minimizes pitch-roll bias and yaw bias basedon the estimates provided by the pitch-roll bending module 402 and theyaw bending module 404. For example, the mitigation module 406 is ableto minimize yaw bias between the VIO depth and the stereo-depthalgorithm results by correcting the yaw estimation. The correctedconfiguration is then communicated to the AR/VR application 210 fordisplay content based the corrected configuration.

FIG. 5 is a block diagram illustrating a pitch-roll bending module inaccordance with one example embodiment. The pitch-roll bending module402 includes a stereo frame access module 502, a feature projectionmodule 504, a feature alignment module 506, and a pitch-roll biasestimation module 508.

The stereo frame access module 502 accesses a first image from a firstcamera (e.g., left side) of the optical sensors 212 and a second imagefrom a second camera (e.g., right side) of the optical sensors 212. Inone example, the stereo frame access module 502 determines stereo VIOfeatures of a first stereo frame.

The feature projection module 504 accesses stereo VIO data (e.g., 3Dpoints, pose) from the VIO system 302. In one example, the featureprojection module 504 projects the stereo VIO features from the opticalsensors 212 to a rectified two-dimensional coordinate system.

The feature alignment module 506 determines whether corresponding stereoVIO features (from a left side image and a right side image) lie on thesame raster line. For example, the feature alignment module 506determines that a VIO feature from the left side image does not align inthe same raster line with the same corresponding VIO feature from theright side image. In that case, the feature alignment module 506triggers a compensation process at the pitch-roll bias estimation module508.

The pitch-roll bias estimation module 508 estimates a pitch-roll biasbased on the misalignment between the left side VIO feature and theright side VIO feature with respect to a raster line of one of the rightside or left side image. The pitch-roll bias estimation module 508computes the pitch-roll bias based on a misalignment in the projectedstereo VIO features in the two-dimensional coordinate system. Thepitch-roll bias estimation module 508 provides the estimated pitch-rollbias to the mitigation module 406 for rectification.

FIG. 6 is a block diagram illustrating the yaw bending module 404 inaccordance with one example embodiment. The yaw bending module 404includes a disparity map module 602, a yaw bias estimation module 608,and a yaw update module 610.

The disparity map module 602 accesses VIO data from the VIO system 302and depth data from the depth map system 304. The disparity map module602 identifies a disparity or misalignment (for a feature point) betweenthe VIO data and the depth data. In one example embodiment, thedisparity map module 602 includes a 3D landmark projection module 604and a landmark disparity computation module 606.

The 3D landmark projection module 604 projects the 3D feature pointsfrom the VIO data onto a two-dimensional coordinate system (e.g., depthdata for each feature point). For example, the 3D landmark projectionmodule 604 identifies three-dimensional landmarks of the first stereoframe using the VIO data from the VIO system 302. The 3D landmarkprojection module 604 projects the three-dimensional landmarks on atwo-dimensional disparity map. The two-dimensional disparity mapindicates two-dimensional locations and corresponding depth values oflandmarks.

The landmark disparity computation module 606 determines a depthmisalignment, for each feature point or landmark, between a stereo depthfrom the depth data and a VIO depth from the VIO data. In one example,the landmark disparity computation module 606 computes a depth biasvalue for each landmark in the first stereo frame.

The yaw bias estimation module 608 estimates a yaw bias based on thedepth misalignment of the feature points. In one example, the yaw biasestimation module 608 computes a yaw bias value based on the depth biasvalue for each landmark.

The yaw update module 610 rectifies the yaw bias based on the depthmisalignment and provides an updated configuration setting to the depthmap system 304. The updated configuration setting includes the yaw biasestimate. In one example, the yaw update module 610 updates arectification map based on the total yaw bending estimate and requeststhe depth map system 304 to compute a depth map based on therectification map.

The following pseudo code illustrates an example implementation of theoperations of the disparity map module 602:

1. Call [FeatureCount, RotationMean, RotationVariance] =EstimateYawRotationBias( ) 2. if FeatureCount is larger than a threshold3. Call UpdateYaw(RotationMean, RotationVariance)

The following pseudo code illustrates an example implementation of theoperations of the yaw bias estimation module 608:

Yaw estimation procedure EstimateYawRotationBias( ): Let featureCount =0Let rotationMean =0 Let rotationMeanSquared =0 for each feature fromVIO: if feature is not valid continue to next feature # Match it againstthe existing disparity map [X,Y]=projectFeatureToDepthSystemCoordinates() Assign sampledDisparityVal = sample disparity at X,Y # Convert depthfrom VIO to equivalent disparity Let f be the focal length of the cameraLet B be the baseline between the two cameras Let vioDepth be the depthvalue of the VIO feature assign vioDisparity=f*B/vioDepth # Computeequivalent rotation (implemented by the equations above) AssignEquivalentRotation = ComputeEquivalentRotation(X, d1=sampledDisparityVal,d2=vioDisparity) if abs(EquivalentRotation) is smaller a thresholdAssign featureCount =featureCount+1 Assign rotationMean =rotationMea+EquivalentRotation Assign rotationMeanSquared =rotationMeanSquared+EquivalentRotation*EquivalentRotation AssignrotationMean =rotationMean /featureCount Assign rotationMeanSquaredrotationMeanSquared /featureCount Assign RotationVariance =rotationMeanSquared − rotationMean * rotationMean; return [featureCount,rotationMean, RotationVariance]

The following pseudo code illustrates an example implementation of theoperations of the yaw update module 610:

Yaw update procedure UpdateYaw( ): 1. AssignFilteredYawBias=Filter(RotationMean,RotationVariance) 2. ifFilteredYawBias is larger than a threshold 3. update the total yawbending estimation

FIG. 7 is a block diagram illustrating a corrected depth frame inaccordance with one embodiment. A VIO t-1 702 illustrates VIO data fromVIO system 302 for a frame taken at time t-1. A depth frame t-1 704illustrates stereo depth data of the same frame taken at the time t-1.The bending correction module 216 determines a corrected yaw 706 basedon estimated yaw bias (based a comparison of depth data for featurepoints from the VIO data and the stereo depth data. The depth map system304 receives the updated configuration setting that includes theestimated yaw bias and rectifies its yaw bias. The depth map system 304generates a depth frame at time t (depth frame t 708) based on therectified yaw bias.

FIG. 8 is a flow diagram illustrating a method 800 for adjusting stereoVIO features in accordance with one example embodiment. Operations inthe method 800 may be performed by the bending correction module 216,using components (e.g., modules, engines) described above with respectto FIG. 4. Accordingly, the method 800 is described by way of examplewith reference to the pitch-roll bending module 402. However, it shallbe appreciated that at least some of the operations of the method 800may be deployed on various other hardware configurations or be performedby similar components residing elsewhere.

In block 802, the pitch-roll bending module 402 accesses a previousstereo frame. In block 804, the pitch-roll bending module 402 determinesstereo VIO features from the previous stereo frame. In block 806, thepitch-roll bending module 402 projects stereo VIO features to the depthcoordinate system. In decision block 808, the pitch-roll bending module402 determines whether the stereo VIO features are aligned. In block810, the pitch-roll bending module 402 computes pitch-roll bias. Inblock 812, the pitch-roll bending module 402 adjusts the depthcoordinate system in a current stereo frame with the pitch-roll bias.

It is to be noted that other embodiments may use different sequencing,additional or fewer operations, and different nomenclature orterminology to accomplish similar functions. In some embodiments,various operations may be performed in parallel with other operations,either in a synchronous or asynchronous manner. The operations describedherein were chosen to illustrate some principles of operations in asimplified form.

FIG. 9 is a flow diagram illustrating a method 900 for adjusting a yawbias in accordance with one example embodiment. Operations in the method900 may be performed by the bending correction module 216, usingcomponents (e.g., modules, engines) described above with respect to FIG.4. Accordingly, the method 900 is described by way of example withreference to the yaw bending module 404. However, it shall beappreciated that at least some of the operations of the method 900 maybe deployed on various other hardware configurations or be performed bysimilar components residing elsewhere.

In block 902, the yaw bending module 404 accesses 3D landmarks from VIOfor time t. In block 904, the yaw bending module 404 generates a 2Dprojection of a 3D landmark on a 2D disparity map. In block 906, the yawbending module 404 provides a depth of the 3D landmark from a depth mapat time t on the disparity map. In block 908, the yaw bending module 404computes a disparity value for the landmark and other landmarks at timet. In block 910, the yaw bending module 404 estimates yaw bias for eachlandmark based on the corresponding disparity values. In block 912, theyaw bending module 404 adjusts the yaw bias based on the estimated yawbias.

FIG. 10 is a flow diagram illustrating a method 1000 for updating thetotal yaw bending estimation in accordance with one example embodiment.Operations in the method 1000 may be performed by the bending correctionmodule 216, using components (e.g., modules, engines) described abovewith respect to FIG. 2. Accordingly, the method 1000 is described by wayof example with reference to the bending correction module 216. However,it shall be appreciated that at least some of the operations of themethod 1000 may be deployed on various other hardware configurations orbe performed by similar components residing elsewhere.

In block 1002, the yaw bending module 404 computes a disparity value foreach landmark in a prior frame at t-1. In block 1004, the yaw bendingmodule 404 computes yaw bias values based on disparity values for eachlandmark of prior frame t-1. In block 1006, the yaw bending module 404computes mean/variance of yaw bias values. In block 1008, the yawbending module 404 filters yaw bias values. In decision block 1010, theyaw bending module 404 determines whether the filtered yaw bias valueexceeds a yaw bias threshold. In block 1012, the yaw bending module 404updates total yaw bending estimation for next frame at time t.

FIG. 11 illustrates misalignment errors resulting from bending of aflexible device. Example 1102 illustrates feature correspondences thatdo not lie on the same raster lines due to pitch/roll bending. Example1104 illustrates z bias 1106 due to yaw bending.

FIG. 12 illustrates a pitch-roll misalignment in accordance with oneembodiment. Example 1202 illustrates corresponding features (between aleft side and a right side) that do not lie on the same raster line dueto bending. Example 1204 illustrates corresponding features that lie onthe same raster line.

FIG. 13 illustrates depth misalignment on a projected two-dimensionalmap in accordance with one embodiment. Example 1302 illustrates adisparity map over time. Example 1302 indicates that the disparity (ofeach feature correspondence) is relatively small (within a threshold)over time. Example 1304 indicates that the disparity remains outside theyaw threshold over time and that the yaw bias is to be updated.

System with Head-Wearable Apparatus

FIG. 14 illustrates a network environment 1400 in which thehead-wearable apparatus 1402 can be implemented according to one exampleembodiment. FIG. 14 is a high-level functional block diagram of anexample head-wearable apparatus 1402 communicatively coupled a mobileclient device 1438 and a server system 1432 via various network 1440.

head-wearable apparatus 1402 includes a camera, such as at least one ofvisible light camera 1412, infrared emitter 1414 and infrared camera1416. The client device 1438 can be capable of connecting withhead-wearable apparatus 1402 using both a communication 1434 and acommunication 1436. client device 1438 is connected to server system1432 and network 1440. The network 1440 may include any combination ofwired and wireless connections.

The head-wearable apparatus 1402 further includes two image displays ofthe image display of optical assembly 1404. The two include oneassociated with the left lateral side and one associated with the rightlateral side of the head-wearable apparatus 1402. The head-wearableapparatus 1402 also includes image display driver 1408, image processor1410, low-power low power circuitry 1426, and high-speed circuitry 1418.The image display of optical assembly 1404 are for presenting images andvideos, including an image that can include a graphical user interfaceto a user of the head-wearable apparatus 1402.

The image display driver 1408 commands and controls the image display ofthe image display of optical assembly 1404. The image display driver1408 may deliver image data directly to the image display of the imagedisplay of optical assembly 1404 for presentation or may have to convertthe image data into a signal or data format suitable for delivery to theimage display device. For example, the image data may be video dataformatted according to compression formats, such as H. 264 (MPEG-4 Part10), HEVC, Theora, Dirac, RealVideo RV40, VP8, VP9, or the like, andstill image data may be formatted according to compression formats suchas Portable Network Group (PNG), Joint Photographic Experts Group(JPEG), Tagged Image File Format (TIFF) or exchangeable image fileformat (Exif) or the like.

As noted above, head-wearable apparatus 1402 includes a frame and stems(or temples) extending from a lateral side of the frame. Thehead-wearable apparatus 1402 further includes a user input device 1406(e.g., touch sensor or push button) including an input surface on thehead-wearable apparatus 1402. The user input device 1406 (e.g., touchsensor or push button) is to receive from the user an input selection tomanipulate the graphical user interface of the presented image.

The components shown in FIG. 14 for the head-wearable apparatus 1402 arelocated on one or more circuit boards, for example a PCB or flexiblePCB, in the rims or temples. Alternatively, or additionally, thedepicted components can be located in the chunks, frames, hinges, orbridge of the head-wearable apparatus 1402. Left and right can includedigital camera elements such as a complementarymetal—oxide—semiconductor (CMOS) image sensor, charge coupled device, acamera lens, or any other respective visible or light capturing elementsthat may be used to capture data, including images of scenes withunknown objects.

The head-wearable apparatus 1402 includes a memory 1422 which storesinstructions to perform a subset or all of the functions describedherein. memory 1422 can also include storage device.

As shown in FIG. 14, high-speed circuitry 1418 includes high-speedprocessor 1420, memory 1422, and high-speed wireless circuitry 1424. Inthe example, the image display driver 1408 is coupled to the high-speedcircuitry 1418 and operated by the high-speed processor 1420 in order todrive the left and right image displays of the image display of opticalassembly 1404. high-speed processor 1420 may be any processor capable ofmanaging high-speed communications and operation of any generalcomputing system needed for head-wearable apparatus 1402. The high-speedprocessor 1420 includes processing resources needed for managinghigh-speed data transfers on communication 1436 to a wireless local areanetwork (WLAN) using high-speed wireless circuitry 1424. In certainexamples, the high-speed processor 1420 executes an operating systemsuch as a LINUX operating system or other such operating system of thehead-wearable apparatus 1402 and the operating system is stored inmemory 1422 for execution. In addition to any other responsibilities,the high-speed processor 1420 executing a software architecture for thehead-wearable apparatus 1402 is used to manage data transfers withhigh-speed wireless circuitry 1424. In certain examples, high-speedwireless circuitry 1424 is configured to implement Institute ofElectrical and Electronic Engineers (IEEE) 802.11 communicationstandards, also referred to herein as Wi-Fi. In other examples, otherhigh-speed communications standards may be implemented by high-speedwireless circuitry 1424.

The low power wireless circuitry 1430 and the high-speed wirelesscircuitry 1424 of the head-wearable apparatus 1402 can include shortrange transceivers (Bluetooth™) and wireless wide, local, or wide areanetwork transceivers (e.g., cellular or WiFi). The client device 1438,including the transceivers communicating via the communication 1434 andcommunication 1436, may be implemented using details of the architectureof the head-wearable apparatus 1402, as can other elements of network1440.

The memory 1422 includes any storage device capable of storing variousdata and applications, including, among other things, camera datagenerated by the left and right, infrared camera 1416, and the imageprocessor 1410, as well as images generated for display by the imagedisplay driver 1408 on the image displays of the image display ofoptical assembly 1404. While memory 1422 is shown as integrated withhigh-speed circuitry 1418, in other examples, memory 1422 may be anindependent standalone element of the head-wearable apparatus 1402. Incertain such examples, electrical routing lines may provide a connectionthrough a chip that includes the high-speed processor 1420 from theimage processor 1410 or low power processor 1428 to the memory 1422. Inother examples, the high-speed processor 1420 may manage addressing ofmemory 1422 such that the low power processor 1428 will boot thehigh-speed processor 1420 any time that a read or write operationinvolving memory 1422 is needed.

As shown in FIG. 14, the low power processor 1428 or high-speedprocessor 1420 of the head-wearable apparatus 1402 can be coupled to thecamera (visible light camera 1412; infrared emitter 1414, or infraredcamera 1416), the image display driver 1408, the user input device 1406(e.g., touch sensor or push button), and the memory 1422.

The head-wearable apparatus 1402 is connected with a host computer. Forexample, the head-wearable apparatus 1402 is paired with the clientdevice 1438 via the communication 1436 or connected to the server system1432 via the network 1440. server system 1432 may be one or morecomputing devices as part of a service or network computing system, forexample, that include a processor, a memory, and network communicationinterface to communicate over the network 1440 with the client device1438 and head-wearable apparatus 1402.

The client device 1438 includes a processor and a network communicationinterface coupled to the processor. The network communication interfaceallows for communication over the network 1440, communication 1434 orcommunication 1436. client device 1438 can further store at leastportions of the instructions for generating a binaural audio content inthe client device 1438's memory to implement the functionality describedherein.

Output components of the head-wearable apparatus 1402 include visualcomponents, such as a display such as a liquid crystal display (LCD), aplasma display panel (PDP), a light emitting diode (LED) display, aprojector, or a waveguide. The image displays of the optical assemblyare driven by the image display driver 1408. The output components ofthe head-wearable apparatus 1402 further include acoustic components(e.g., speakers), haptic components (e.g., a vibratory motor), othersignal generators, and so forth. The input components of thehead-wearable apparatus 1402, the client device 1438, and server system1432, such as the user input device 1406, may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and force of touches or touch gestures, orother tactile input components), audio input components (e.g., amicrophone), and the like.

The head-wearable apparatus 1402 may optionally include additionalperipheral device elements. Such peripheral device elements may includebiometric sensors, additional sensors, or display elements integratedwith head-wearable apparatus 1402. For example, peripheral deviceelements may include any I/O components including output components,motion components, position components, or any other such elementsdescribed herein.

For example, the biometric components include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram based identification), and the like. The motioncomponents include acceleration sensor components (e.g., accelerometer),gravitation sensor components, rotation sensor components (e.g.,gyroscope), and so forth. The position components include locationsensor components to generate location coordinates (e.g., a GlobalPositioning System (GPS) receiver component), WiFi or Bluetooth™transceivers to generate positioning system coordinates, altitude sensorcomponents (e.g., altimeters or barometers that detect air pressure fromwhich altitude may be derived), orientation sensor components (e.g.,magnetometers), and the like. Such positioning system coordinates canalso be received over and communication 1436 from the client device 1438via the low power wireless circuitry 1430 or high-speed wirelesscircuitry 1424.

FIG. 15 is a block diagram 1500 illustrating a software architecture1504, which can be installed on any one or more of the devices describedherein. The software architecture 1504 is supported by hardware such asa machine 1502 that includes Processors 1520, memory 1526, and I/OComponents 1538. In this example, the software architecture 1504 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1504 includes layerssuch as an operating system 1512, libraries 1510, frameworks 1508, andapplications 1506. Operationally, the applications 1506 invoke API calls1550 through the software stack and receive messages 1552 in response tothe API calls 1550.

The operating system 1512 manages hardware resources and provides commonservices. The operating system 1512 includes, for example, a kernel1514, services 1516, and drivers 1522. The kernel 1514 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1514 provides memory management, Processormanagement (e.g., scheduling), Component management, networking, andsecurity settings, among other functionality. The services 1516 canprovide other common services for the other software layers. The drivers1522 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1522 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., Universal Serial Bus(USB) drivers), WI-FI® drivers, audio drivers, power management drivers,and so forth.

The libraries 1510 provide a low-level common infrastructure used by theapplications 1506. The libraries 1510 can include system libraries 1518(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1510 can include APIlibraries 1524 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 1510 can also include a widevariety of other libraries 1528 to provide many other APIs to theapplications 1506.

The frameworks 1508 provide a high-level common infrastructure that isused by the applications 1506. For example, the frameworks 1508 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 1508 canprovide a broad spectrum of other APIs that can be used by theapplications 1506, some of which may be specific to a particularoperating system or platform.

In an example embodiment, the applications 1506 may include a homeapplication 1536, a contacts application 1530, a browser application1532, a book reader application 1534, a location application 1542, amedia application 1544, a messaging application 1546, a game application1548, and a broad assortment of other applications such as a third-partyapplication 1540. The applications 1506 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 1506, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the third-partyapplication 1540 (e.g., an application developed using the ANDROID™ orIOS™ software development kit (SDK) by an entity other than the vendorof the particular platform) may be mobile software running on a mobileoperating system such as IOS™, ANDROID™, WINDOWS® Phone, or anothermobile operating system. In this example, the third-party application1540 can invoke the API calls 1550 provided by the operating system 1512to facilitate functionality described herein.

FIG. 16 is a diagrammatic representation of the machine 1600 withinwhich instructions 1608 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 1600to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 1608 may cause the machine 1600to execute any one or more of the methods described herein. Theinstructions 1608 transform the general, non-programmed machine 1600into a particular machine 1600 programmed to carry out the described andillustrated functions in the manner described. The machine 1600 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 1600 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1600 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a PDA, an entertainment media system, a cellulartelephone, a smart phone, a mobile device, a wearable device (e.g., asmart watch), a smart home device (e.g., a smart appliance), other smartdevices, a web appliance, a network router, a network switch, a networkbridge, or any machine capable of executing the instructions 1608,sequentially or otherwise, that specify actions to be taken by themachine 1600. Further, while only a single machine 1600 is illustrated,the term “machine” shall also be taken to include a collection ofmachines that individually or jointly execute the instructions 1608 toperform any one or more of the methodologies discussed herein.

The machine 1600 may include Processors 1602, memory 1604, and I/OComponents 1642, which may be configured to communicate with each othervia a bus 1644. In an example embodiment, the Processors 1602 (e.g., aCentral Processing Unit (CPU), a Reduced Instruction Set Computing(RISC) Processor, a Complex Instruction Set Computing (CISC) Processor,a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), anASIC, a Radio-Frequency Integrated Circuit (RFIC), another Processor, orany suitable combination thereof) may include, for example, a Processor1606 and a Processor 1610 that execute the instructions 1608. The term“Processor” is intended to include multi-core Processors that maycomprise two or more independent Processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.16 shows multiple Processors 1602, the machine 1600 may include a singleProcessor with a single core, a single Processor with multiple cores(e.g., a multi-core Processor), multiple Processors with a single core,multiple Processors with multiples cores, or any combination thereof.

The memory 1604 includes a main memory 1612, a static memory 1614, and astorage unit 1616, both accessible to the Processors 1602 via the bus1644. The main memory 1604, the static memory 1614, and storage unit1616 store the instructions 1608 embodying any one or more of themethodologies or functions described herein. The instructions 1608 mayalso reside, completely or partially, within the main memory 1612,within the static memory 1614, within machine-readable medium 1618within the storage unit 1616, within at least one of the Processors 1602(e.g., within the Processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 1600.

The I/O Components 1642 may include a wide variety of Components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/OComponents 1642 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O Components 1642 mayinclude many other Components that are not shown in FIG. 16. In variousexample embodiments, the I/O Components 1642 may include outputComponents 1628 and input Components 1630. The output Components 1628may include visual Components (e.g., a display such as a plasma displaypanel (PDP), a light emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticComponents (e.g., speakers), haptic Components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The inputComponents 1630 may include alphanumeric input Components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input Components),point-based input Components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput Components (e.g., a physical button, a touch screen that provideslocation and/or force of touches or touch gestures, or other tactileinput Components), audio input Components (e.g., a microphone), and thelike.

In further example embodiments, the I/O Components 1642 may includebiometric Components 1632, motion Components 1634, environmentalComponents 1636, or position Components 1638, among a wide array ofother Components. For example, the biometric Components 1632 includeComponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram-basedidentification), and the like. The motion Components 1634 includeacceleration sensor Components (e.g., accelerometer), gravitation sensorComponents, rotation sensor Components (e.g., gyroscope), and so forth.The environmental Components 1636 include, for example, illuminationsensor Components (e.g., photometer), temperature sensor Components(e.g., one or more thermometers that detect ambient temperature),humidity sensor Components, pressure sensor Components (e.g.,barometer), acoustic sensor Components (e.g., one or more microphonesthat detect background noise), proximity sensor Components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other Componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position Components 1638 includelocation sensor Components (e.g., a GPS receiver Component), altitudesensor Components (e.g., altimeters or barometers that detect airpressure from which altitude may be derived), orientation sensorComponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O Components 1642 further include communication Components 1640operable to couple the machine 1600 to a network 1620 or devices 1622via a coupling 1624 and a coupling 1626, respectively. For example, thecommunication Components 1640 may include a network interface Componentor another suitable device to interface with the network 1620. Infurther examples, the communication Components 1640 may include wiredcommunication Components, wireless communication Components, cellularcommunication Components, Near Field Communication (NFC) Components,Bluetooth® Components (e.g., Bluetooth® Low Energy), Wi-Fi® Components,and other communication Components to provide communication via othermodalities. The devices 1622 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication Components 1640 may detect identifiers orinclude Components operable to detect identifiers. For example, thecommunication Components 1640 may include Radio Frequency Identification(RFID) tag reader Components, NFC smart tag detection Components,optical reader Components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection Components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication Components1640, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., memory 1604, main memory 1612, static memory1614, and/or memory of the Processors 1602) and/or storage unit 1616 maystore one or more sets of instructions and data structures (e.g.,software) embodying or used by any one or more of the methodologies orfunctions described herein. These instructions (e.g., the instructions1608), when executed by Processors 1602, cause various operations toimplement the disclosed embodiments.

The instructions 1608 may be transmitted or received over the network1620, using a transmission medium, via a network interface device (e.g.,a network interface Component included in the communication Components1640) and using any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1608 may be transmitted or received using a transmission medium via thecoupling 1626 (e.g., a peer-to-peer coupling) to the devices 1622.

Although an embodiment has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader scope of the present disclosure. Accordingly, the specificationand drawings are to be regarded in an illustrative rather than arestrictive sense. The accompanying drawings that form a part hereof,show by way of illustration, and not of limitation, specific embodimentsin which the subject matter may be practiced. The embodimentsillustrated are described in sufficient detail to enable those skilledin the art to practice the teachings disclosed herein. Other embodimentsmay be utilized and derived therefrom, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure. This Detailed Description, therefore, is not to betaken in a limiting sense, and the scope of various embodiments isdefined only by the appended claims, along with the full range ofequivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus the following claims arehereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment.

EXAMPLES

Example 1 is a method for correcting a bending of a flexible devicecomprising: accessing feature data of a first stereo frame that isgenerated by stereo optical sensors of the flexible device, the featuredata generated based on a visual-inertial odometry (VIO) system of theflexible device; accessing depth map data of the first stereo frame, thedepth map data generated based on a depth map system of the flexibledevice; estimating a pitch-roll bias and a yaw bias based on thefeatures data and the depth map data of the first stereo frame; andgenerating a second stereo frame after the first stereo frame, thesecond stereo frame based on the pitch-roll bias and the yaw bias of thefirst stereo frame.

Example 2 includes example 1, wherein estimating the pitch-roll biasfurther comprises: determining stereo VIO features of the first stereoframe; projecting the stereo VIO features to a two-dimensionalcoordinate system; determining that the projected stereo VIO featuresare not aligned in the two-dimensional coordinate system; and inresponse to determining that the projected stereo VIO features are notaligned in the two-dimensional coordinate system, computing thepitch-roll bias based on a misalignment in the projected stereo VIOfeatures in the two-dimensional coordinate system.

Example 3 includes example 2, wherein determining that the projectedstereo VIO features are not aligned in the two-dimensional coordinatesystem further comprise: identifying a first feature in a left frame ofthe first stereo frame; identifying a second feature in a right frame ofthe first stereo frame, the second feature corresponding to the firstfeature; and determining that the first feature and the second featurelie on different raster lines of the two-dimensional coordinate system.

Example 4 includes example 2, wherein generating the second stereo framefurther comprises: identifying a first feature in a left frame of thesecond stereo frame; identifying a second feature in a right frame ofthe second stereo frame, the second feature corresponding to the firstfeature; and rectifying a location of the first feature in the leftframe or the second feature in the right frame based on the pitch-rollbias of the first stereo frame.

Example 5 includes example 1, wherein estimating the yaw bias furthercomprises: identifying three-dimensional landmarks of the first stereoframe using the VIO system; projecting the three-dimensional landmarkson a two-dimensional disparity map, the two-dimensional disparity mapindicating two-dimensional locations and corresponding depth values oflandmarks; computing a depth bias value for each landmark in the firststereo frame; and computing a yaw bias value based on the depth biasvalue for each landmark.

Example 6 includes example 5, wherein projecting the three-dimensionallandmarks further comprises: depicting a first landmark of the firststereo frame using the VIO system at a first depth value on thetwo-dimensional disparity map; and depicting the first landmark of thefirst stereo frame using the depth map data at a second depth value onthe two-dimensional disparity map, wherein the depth bias value of thefirst landmark is a difference between the first depth value and thesecond depth value.

Example 7 includes example 5, further comprising: computing statisticsof the yaw bias values of a plurality of landmarks in the first stereoframe; computing a filtered yaw bias value based on the statistics ofthe yaw bias values; determining that the filtered yaw bias valueexceeds a yaw bias threshold; in response to determining that thefiltered yaw bias value exceeds the yaw bias threshold, updating a totalyaw bending estimate for the second stereo frame; updating arectification map based on the total yaw bending estimate; and computinga depth map based on the rectification map.

Example 8 includes example 1, wherein the VIO system is configured toidentify a pose of the flexible device based on sensor data frominertial sensors and optical sensors of the flexible device.

Example 9 includes example 1, wherein the depth map system determines adepth of a pixel in the first stereo frame based on a triangulation ofthe pixel depicted in a right side of the first stereo frame and in aleft side of the first stereo frame.

Example 10 includes example 1, further comprising: generating virtualcontent in the first stereo frame; and adjusting a display of thevirtual content in the second stereo frame based on the pitch-roll biasand yaw bias of the first stereo frame.

Example 11 is computing apparatus comprising: a processor; and a memorystoring instructions that, when executed by the processor, configure theapparatus to: access feature data of a first stereo frame that isgenerated by stereo optical sensors of a flexible device, the featuredata generated based on a visual-inertial odometry (VIO) system of theflexible device, access depth map data of the first stereo frame, thedepth map data generated based on a depth map system of the flexibledevice; estimate a pitch-roll bias and a yaw bias based on the featuresdata and the depth map data of the first stereo frame; and generate asecond stereo frame after the first stereo frame, the second stereoframe based on the pitch-roll bias and the yaw bias of the first stereoframe.

Example 12 includes example 11, wherein estimating the pitch-roll biasfurther comprises: determine stereo VIO features of the first stereoframe; project the stereo VIO features to a two-dimensional coordinatesystem; determine that the projected stereo VIO features are not alignedin the two-dimensional coordinate system; and in response to determiningthat the projected stereo VIO features are not aligned in thetwo-dimensional coordinate system, compute the pitch-roll bias based ona misalignment in the projected stereo VIO features in thetwo-dimensional coordinate system.

Example 13 includes example 12, wherein determining that the projectedstereo VIO features are not aligned in the two-dimensional coordinatesystem further comprise: identify a first feature in a left frame of thefirst stereo frame; identify a second feature in a right frame of thefirst stereo frame, the second feature corresponding to the firstfeature; and determine that the first feature and the second feature lieon different raster lines of the two-dimensional coordinate system.

Example 14 includes example 12, wherein generating the second stereoframe further comprises: identify a first feature in a left frame of thesecond stereo frame; identify a second feature in a right frame of thesecond stereo frame, the second feature corresponding to the firstfeature; and rectify a location of the first feature in the left frameor the second feature in the right frame based on the pitch-roll bias ofthe first stereo frame.

Example 15 includes example 11, wherein estimating the yaw bias furthercomprises: identify three-dimensional landmarks of the first stereoframe using the VIO system; project the three-dimensional landmarks on atwo-dimensional disparity map, the two-dimensional disparity mapindicating two-dimensional locations and corresponding depth values oflandmarks; compute a depth bias value for each landmark in the firststereo frame; and compute a yaw bias value based on the depth bias valuefor each landmark.

Example 16 includes example 15, wherein projecting the three-dimensionallandmarks further comprises: depict a first landmark of the first stereoframe using the VIO system at a first depth value on the two-dimensionaldisparity map; and depict the first landmark of the first stereo frameusing the depth map data at a second depth value on the two-dimensionaldisparity map, wherein the depth bias value of the first landmark is adifference between the first depth value and the second depth value.

Example 17 includes example 15, wherein the instructions furtherconfigure the apparatus to: compute statistics of the yaw bias values ofa plurality of landmarks in the first stereo frame; compute a filteredyaw bias value based on the statistics of the yaw bias values; determinethat the filtered yaw bias value exceeds a yaw bias threshold; inresponse to determining that the filtered yaw bias value exceeds the yawbias threshold, update a total yaw bending estimate for the secondstereo frame; update a rectification map based on the total yaw bendingestimate; and compute a depth map based on the rectification map.

Example 18 includes example 11, wherein the VIO system is configured toidentify a pose of the flexible device based on sensor data frominertial sensors and optical sensors of the flexible device.

Example 19 includes example 11, wherein the depth map system determinesa depth of a pixel in the first stereo frame based on a triangulation ofthe pixel depicted in a right side of the first stereo frame and in aleft side of the first stereo frame.

Example 20 is a non-transitory computer-readable storage medium, thecomputer-readable storage medium including instructions that whenexecuted by a computer, cause the computer to: access feature data of afirst stereo frame that is generated by stereo optical sensors of theflexible device, the feature data generated based on a visual-inertialodometry (VIO) system of the flexible device, access depth map data ofthe first stereo frame, the depth map data generated based on a depthmap system of the flexible device; estimate a pitch-roll bias and a yawbias based on the features data and the depth map data of the firststereo frame; and generate a second stereo frame after the first stereoframe, the second stereo frame based on the pitch-roll bias and the yawbias of the first stereo frame.

What is claimed is:
 1. A method for correcting a bending of a flexibledevice comprising: accessing feature data of a first stereo frame thatis generated by stereo optical sensors of the flexible device, thefeature data generated based on a visual-inertial odometry (VIO) systemof the flexible device; accessing depth map data of the first stereoframe, the depth map data generated based on a depth map system of theflexible device; estimating a pitch-roll bias and a yaw bias based onthe features data and the depth map data of the first stereo frame; andgenerating a second stereo frame after the first stereo frame, thesecond stereo frame based on the pitch-roll bias and the yaw bias of thefirst stereo frame.
 2. The method of claim 1, wherein estimating thepitch-roll bias further comprises: determining stereo VIO features ofthe first stereo frame; projecting the stereo VIO features to atwo-dimensional coordinate system; determining that the projected stereoVIO features are not aligned in the two-dimensional coordinate system;and in response to determining that the projected stereo VIO featuresare not aligned in the two-dimensional coordinate system, computing thepitch-roll bias based on a misalignment in the projected stereo VIOfeatures in the two-dimensional coordinate system.
 3. The method ofclaim 2, wherein determining that the projected stereo VIO features arenot aligned in the two-dimensional coordinate system further comprise:identifying a first feature in a left frame of the first stereo frame;identifying a second feature in a right frame of the first stereo frame,the second feature corresponding to the first feature; and determiningthat the first feature and the second feature lie on different rasterlines of the two-dimensional coordinate system.
 4. The method of claim2, wherein generating the second stereo frame further comprises:identifying a first feature in a left frame of the second stereo frame;identifying a second feature in a right frame of the second stereoframe, the second feature corresponding to the first feature; andrectifying a location of the first feature in the left frame or thesecond feature in the right frame based on the pitch-roll bias of thefirst stereo frame.
 5. The method of claim 1, wherein estimating the yawbias further comprises: identifying three-dimensional landmarks of thefirst stereo frame using the VIO system; projecting thethree-dimensional landmarks on a two-dimensional disparity map, thetwo-dimensional disparity map indicating two-dimensional locations andcorresponding depth values of landmarks; computing a depth bias valuefor each landmark in the first stereo frame; and computing a yaw biasvalue based on the depth bias value for each landmark.
 6. The method ofclaim 5, wherein projecting the three-dimensional landmarks furthercomprises: depicting a first landmark of the first stereo frame usingthe VIO system at a first depth value on the two-dimensional disparitymap; and depicting the first landmark of the first stereo frame usingthe depth map data at a second depth value on the two-dimensionaldisparity map, wherein the depth bias value of the first landmark is adifference between the first depth value and the second depth value. 7.The method of claim 5, further comprising: computing statistics of theyaw bias values of a plurality of landmarks in the first stereo frame;computing a filtered yaw bias value based on the statistics of the yawbias values; determining that the filtered yaw bias value exceeds a yawbias threshold; in response to determining that the filtered yaw biasvalue exceeds the yaw bias threshold, updating a total yaw bendingestimate for the second stereo frame; updating a rectification map basedon the total yaw bending estimate; and computing a depth map based onthe rectification map.
 8. The method of claim 1, wherein the VIO systemis configured to identify a pose of the flexible device based on sensordata from inertial sensors and optical sensors of the flexible device.9. The method of claim 1, wherein the depth map system determines adepth of a pixel in the first stereo frame based on a triangulation ofthe pixel depicted in a right side of the first stereo frame and in aleft side of the first stereo frame.
 10. The method of claim 1, furthercomprising: generating virtual content in the first stereo frame; andadjusting a display of the virtual content in the second stereo framebased on the pitch-roll bias and yaw bias of the first stereo frame. 11.A computing apparatus comprising: a processor; and a memory storinginstructions that, when executed by the processor, configure theapparatus to: access feature data of a first stereo frame that isgenerated by stereo optical sensors of a flexible device, the featuredata generated based on a visual-inertial odometry (VIO) system of theflexible device; access depth map data of the first stereo frame, thedepth map data generated based on a depth map system of the flexibledevice; estimate a pitch-roll bias and a yaw bias based on the featuresdata and the depth map data of the first stereo frame; and generate asecond stereo frame after the first stereo frame, the second stereoframe based on the pitch-roll bias and the yaw bias of the first stereoframe.
 12. The computing apparatus of claim 11, wherein estimating thepitch-roll bias further comprises: determine stereo VIO features of thefirst stereo frame; project the stereo VIO features to a two-dimensionalcoordinate system; determine that the projected stereo VIO features arenot aligned in the two-dimensional coordinate system; and in response todetermining that the projected stereo VIO features are not aligned inthe two-dimensional coordinate system, compute the pitch-roll bias basedon a misalignment in the projected stereo VIO features in thetwo-dimensional coordinate system.
 13. The computing apparatus of claim12, wherein determining that the projected stereo VIO features are notaligned in the two-dimensional coordinate system further comprise:identify a first feature in a left frame of the first stereo frame;identify a second feature in a right frame of the first stereo frame,the second feature corresponding to the first feature; and determinethat the first feature and the second feature lie on different rasterlines of the two-dimensional coordinate system.
 14. The computingapparatus of claim 12, wherein generating the second stereo framefurther comprises: identify a first feature in a left frame of thesecond stereo frame; identify a second feature in a right frame of thesecond stereo frame, the second feature corresponding to the firstfeature; and rectify a location of the first feature in the left frameor the second feature in the right frame based on the pitch-roll bias ofthe first stereo frame.
 15. The computing apparatus of claim 11, whereinestimating the yaw bias further comprises: identify three-dimensionallandmarks of the first stereo frame using the VIO system; project thethree-dimensional landmarks on a two-dimensional disparity map, thetwo-dimensional disparity map indicating two-dimensional locations andcorresponding depth values of landmarks; compute a depth bias value foreach landmark in the first stereo frame; and compute a yaw bias valuebased on the depth bias value for each landmark.
 16. The computingapparatus of claim 15, wherein projecting the three-dimensionallandmarks further comprises: depict a first landmark of the first stereoframe using the VIO system at a first depth value on the two-dimensionaldisparity map; and depict the first landmark of the first stereo frameusing the depth map data at a second depth value on the two-dimensionaldisparity map, wherein the depth bias value of the first landmark is adifference between the first depth value and the second depth value. 17.The computing apparatus of claim 15, wherein the instructions furtherconfigure the apparatus to: compute statistics of the yaw bias values ofa plurality of landmarks in the first stereo frame; compute a filteredyaw bias value based on the statistics of the yaw bias values; determinethat the filtered yaw bias value exceeds a yaw bias threshold; inresponse to determining that the filtered yaw bias value exceeds the yawbias threshold, update a total yaw bending estimate for the secondstereo frame; update a rectification map based on the total yaw bendingestimate; and compute a depth map based on the rectification map. 18.The computing apparatus of claim 11, wherein the VIO system isconfigured to identify a pose of the flexible device based on sensordata from inertial sensors and optical sensors of the flexible device.19. The computing apparatus of claim 11, wherein the depth map systemdetermines a depth of a pixel in the first stereo frame based on atriangulation of the pixel depicted in a right side of the first stereoframe and in a left side of the first stereo frame.
 20. A non-transitorycomputer-readable storage medium, the computer-readable storage mediumincluding instructions that when executed by a computer, cause thecomputer to: access feature data of a first stereo frame that isgenerated by stereo optical sensors of a flexible device, the featuredata generated based on a visual-inertial odometry (VIO) system of theflexible device; access depth map data of the first stereo frame, thedepth map data generated based on a depth map system of the flexibledevice; estimate a pitch-roll bias and a yaw bias based on the featuresdata and the depth map data of the first stereo frame; and generate asecond stereo frame after the first stereo frame, the second stereoframe based on the pitch-roll bias and the yaw bias of the first stereoframe.